Towards a Realistic Method to Estimate Cannabis Production in Industrialized Countries
Bibliographic record
Abstract
Among the many difficulties with estimating the size of the cannabis industry is that suitable methodologies for estimating large-scale outdoor illegal drug production in developing countries cannot be used to estimate indoor production in industrialized countries. This article proposes a new approach that overcomes some of these difficulties. The case study is a mature cannabis cultivation industry, located in the province Quebec, Canada. Starting from capture-recapture estimates of the prevalence of growers, the approach combines police and fieldwork data sources on the dynamics of the cultivation industry to correct for typical errors in the assumed productivity rates of different kinds of cultivation sites. Using three different approaches to productivity (ounces-per-plant, yield-per-lamp, yield-per-watt) it was estimated that Quebec cannabis production was approximately 300 tons in 2002; 11% was seized by the police, 33% was consumed within the province, and 56% was potentially exported to the U.S. and to other Canadian provinces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".